People like having maps so that they know where they are and so they can figure out how to get from where they are to somewhere else. People also like their mobile devices (e.g., cellular telephones). One use for mobile devices is to provide maps, location services, direction services, and other positioning services. Conventionally, positioning services for mobile devices have been supported by a global positioning system (GPS). GPS works well outdoors and when sufficient satellites are in the line of sight of the device. However, conventional GPS based positioning or map services may not function when line of sight to a sufficient number of satellites is not available. Losing line of sight to a sufficient number or type of satellites may lead to the presence of GPS “dead zones” in locations like inside buildings, under bridges, underground, and during GPS blackouts. Even though a person or machine is in a GPS dead zone (e.g., area where GPS does not provide satisfactory performance), the person may still want to have an accurate position on a map and the ability to receive directions. For example, people may like to have maps and positioning services available on their mobile devices while inside a shopping mall, while inside a convention center, while inside a casino, while inside an office building, while down in a sewer network, or while in other locations that may experience a GPS dead zone.
Since users of personal devices spend time in GPS dead zones, attention has already been paid to location services that will function on personal devices even when “indoors” (e.g., in a GPS dead zone). These location services have typically relied on performing trilateration, triangulation, dead reckoning, or other positioning approaches based on other information that may be available. The other information has included radio frequency (RF) signals (e.g., Wi-Fi signals, cellular telephone signals), information from accelerometers, information from barometers, and information from other sensors. These conventional location services have typically relied on a coupling between physical co-ordinates on an indoor map and a set of sensor data acquired over time from devices that have passed through the area covered by the indoor map.
Approaches for collecting sensor data from which indoor maps can be created have included crowd-sourced approaches, ad hoc approaches, planned approaches, grid-by-grid approaches, and other approaches. However, these conventional approaches appear to tightly couple the acquisition of sensor-based location information to physical co-ordinates (e.g., latitude/longitude, x/y/z) on pre-defined maps. The maps of the physical realities of our geography are constantly changing. Thus, sensor readings that are tightly coupled to physical co-ordinate based maps may become obsolete as the maps of the physical realities change.
One challenge for indoor positioning systems involves collecting sensor data and accurate positions that describe where the sensor data was acquired. Ideally, a surveyor would position themselves at a series of known, fixed points described by a physical co-ordinate (e.g., latitude/longitude) and acquire sensor data at those points. However, since the locations being surveyed are part of a GPS dead zone, GPS is likely unavailable to facilitate positioning the surveyor in the precise survey points required in the tightly coupled physical co-ordinate approach.